This section provides background information related to the present disclosure which is not necessarily prior art.
In the art of controlling powered multi-functional limb prostheses it is known to apply machine learning methods to electromyogram signals acquired from a surface of a remaining portion of a user's limb so as to derive estimates for limb movements intended by the user.
Two main control methods have evolved in this field: classification and regression. Classification methods allow articulation of many degrees of freedom (DOF). The downside of classification is that it does not allow for arbitrary combinations of simultaneous activations across multiple DOFs, which is required to enable a natural appearance of a prosthetic movement (sequential movement estimation). The natural appearance, characterized by simultaneous and proportional control, can be facilitated by employing regression methods (simultaneous movement estimation). However, a controlling capability by using regression methods has been found to be limited to a maximum of four functions, corresponding to two DOFs.